Borehole electrical resistivity modeling using neural networks

Abstract

A neural network approach has been applied to model downhole resistivity tools, i.e., to generate a synthetic tool response for a given earth resistivity model. The microlaterolog (MLL), shallow dual laterolog (DLLs), and deep dual laterolog (DLLd) tools are modeled using neural networks to demonstrate this approach. Efforts have been made to select various neural network parameters, including the type of neural network, the length of input data for training, the number of hidden nodes, and the number of training samples. A modular neural network (MNN) has been selected because it can facilitate the training and prediction of tool responses in formations with large resistivity variations. The input data for training are taken to be the model formation resistivity values sampled over a depth window. The window length is chosen based on the tool lengths. Three different window lengths are used for experiments: 6.1, 9.1, and 30.5 m. We found the longer window lengths generally have higher modeling accuracy for the three different types of logging tools. The number of hidden nodes needed to yield satisfactory training and prediction data varies from 8 to 25, depending on the type of tool and the window length. Up to 30 000 training samples have been collected to train the MNN. Our modeling examples show that the trained MNN can achieve about 90% accuracy for the MLL log response and about 83% accuracy for the DLLs and DLLd responses. The modeling errors can be described roughly with a Gaussian distribution.